The present invention relates to imaging systems and the accuracy of the image displayed to the user. More specifically, the invention relates to dynamically predicting signals of malfunctioning cells or suspected malfunctioning cells in an array of sensing devices, by utilizing other known signals.
Medical diagnostic imaging systems encompass a variety of imaging modalities, such as planar x-rays, ultrasound, magnetic resonance (MR), electron beam tomography (EBT), positron emission tomography (PET), single photon emission computed tomography (SPECT), micro computed tomography, and macro computed tomography imaging systems, and the like. Medical diagnostic imaging systems generate images of an object, such as a patient, through exposure to an energy source, such as x-rays passing through a patient. A generated image may be used for many purposes. For instance, internal defects in an object may be detected. Additionally, changes in internal structure or alignment may be determined. Fluid flow within an object may also be represented. Furthermore, the image may show the presence or absence of components in an object. Information gained from medical diagnostic imaging has applications in many fields, including medicine and manufacturing.
A typical imaging system uses an array of cells to detect an object and then reconstruct and display the detected image. The array includes multiple detector rows. Each detector row includes multiple detector cells, with each detector cell connected to a different data acquisition system (DAS) channel. That is, a DAS channel may be mapped to a detector cell. Each detector cell generates a signal. A large volume array includes a large number of detector cells and DAS channels. As a number of detector cells and DAS channels increases, a probability of failure in a detector cell, DAS channel, or DAS application-specific integrated circuit (ASIC) failure increases. Additionally, as a number of detector cells and DAS channels increases, it would be desirable for components of the imaging system and detector array to become more integrated.
A problem in any one detector cell in either the detector or the DAS channel may cause artifacts in the reconstructed images. Cells with a problem in the detector and/or the DAS channel are called malfunctioning cells. Malfunctioning cells may malfunction in several different ways, such as malfunctioning intermittently, giving a signal that is a certain percentage less accurate than other cells' signals, generating a signal that is a percentage weaker than neighboring cells, and not functioning at all. Any inaccuracies or “artifacts” in the image produced by the imaging system or cells in the imaging system may result in actions taken by physicians, medical practitioners or other observers based on incorrect information.
Increasing the volume coverage of the arrays of cells allows users to image larger objects faster because one sweep of a larger array images more of an object. Increasing a volume coverage of an arrays of cells also images an object more accurately because less time elapses during the imaging process when fewer sweeps are used to image the object. With a continued pursuit of larger volume coverage, a number of detectors and DAS channels increases quickly. As a result, a probability of a malfunctioning cell increases. Replacement of a malfunctioning cell significantly increases the cost of a system. Replacing all failed components on a system with a large number of detector channels may not be economical. In addition, failed components interrupt the operation flow in a hospital. Thus, a system that minimizes image artifacts or significant degradation in image quality due to a malfunctioning cell would be highly desirable.
One method proposed to minimize an impact of a failed detector channel and/or DAS channel utilizes an algorithm which estimates missing projection samples based on neighboring good samples. For the convenience of discussion, assume a projection sample corresponding to detector row n and channel i is defective. A defect may be the result of either detector failure or DAS channel failure, for example. A projection sample for a channel may be denoted by pk(i, n), where k is a projection view index.
A malfunctioning cell, pk(i, n), is in channel i, detector row n and view index k. The malfunctioning cell, pk(i, n), may be estimated by performing linear or bilinear interpolation using neighboring signals. That is, pk(i, n) is estimated using the average of signals pk(i−1, n) and pk(i+1, n), the neighboring lower and higher channels, for linear interpolation. Alternatively, pk(i, n) may be estimated using the average of signals pk(i−1, n), pk(i+1, n), pk(i, n−1), and pk(i, n+1), the neighboring lower and higher channels and the neighboring upper and lower rows, for bilinear interpolation. Although the approaches of linear and bilinear interpolation have computational advantages, both approaches suffer from image artifacts. A more elaborate scheme was proposed in a paper by Tillman Riess, Quirin Spriter, Theobald Fuchs, Thomas von der Haar, and Willi Kalendar entitled “A Fast and Efficient Method for the Correction of Defective Channels in X-ray CT Area Detectors.” The proposed scheme relies on an interpolation in a Sinogram space. That is, the missing projection sample, pk(i, n), is estimated based on the samples of pk−1(i−1, n), pk−1(i, n), and pk−1(i+1, n) taken in a projection view before k, the samples pk(i−1, n) and pk(i+1, n) taken in a then current projection, and the samples pk+1(i−1, n), pk+1(i, n), and pk+1(i+1, n) taken in a future projection k+1. An estimation of a sample for view k uses not only the previously collected views, but also the next view, k+1. That is, an image reconstructor may have to wait for the arrival of the future projection before the current projection may be corrected.
However, future projections may not be the same as current projections because time elapses between images and different imaging angles result in different errors in the image. Especially when taking images of arteries or other parts of the body which change rapidly, projection views in the future are not a reliable source to predict past images because the image may have changed between projection views. Different projection views use different angles from which a detector takes a measurement. A different angle entails that a view travel a different distance. Longer distances cause more inaccuracies in an image because when a view travels a longer distance, more interference results in the image. For these and other reasons, future and previous projection views are an unreliable source for a current projection view's signals. Although the approach proposed by Riess et al. further reduces image artifacts, residual artifacts still remain.
Therefore a need exists for an improved method of minimizing an impact of a malfunctioning cell by utilizing data within the same projection view.